Neural Network Quantization Calibration for Changing Input Conditions
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Solution Overview
Problem
Deep learning neural network models experience performance degradation due to increased quantization errors when actual input data features differ from those used in calibration, particularly in varying external environments such as changes in lighting conditions or time of day.
Innovation Solution
A method and device that detect feature changes in input data caused by external environment changes, updating quantization parameters to adapt to these changes by performing quantization calibration and determining new quantization parameters based on activation maps and bias values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantization is applied to reduce model size and improve computation speed, then power consumption is reduced and computation speed is improved, but quantization error increases when actual input data features differ from calibration data features
Solution Approach 1:
The patent implements dynamic quantization parameter adjustment by detecting feature changes in real-time input data and adapting quantization parameters accordingly. Instead of using fixed quantization parameters determined during calibration, the system continuously monitors activation map statistics and updates quantization parameters to match current input data characteristics, thereby maintaining low quantization error across varying environmental conditions while preserving computation speed benefits.
Solution Approach 2:
The patent changes quantization parameters based on detected feature changes in input data. By monitoring statistical properties of activation maps and adjusting quantization parameters (such as scale and zero-point values) according to these changes, the system adapts to different input data distributions caused by environmental variations, thus reducing quantization error without sacrificing the computational efficiency gained from quantization.
2Ease of operation
If fixed quantization parameters are used based on calibration data distribution, then device complexity is reduced and ease of operation is improved, but performance degrades as input data features change due to external environment
Solution Approach 1:
The patent enables the quantization system to self-adjust by automatically detecting feature changes in input data and updating its own quantization parameters without external intervention. The system monitors activation map statistics and autonomously adapts quantization parameters to current input conditions, maintaining high performance across varying environments while requiring minimal manual reconfiguration, thus achieving both ease of operation and reliability.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors input data features through activation map analysis and uses this information to adjust quantization parameters. By establishing a closed-loop system that detects performance-degrading conditions and automatically corrects them through parameter adaptation, the system maintains reliable performance while keeping the operation simple for users.
3Loss of time
If quantization calibration is performed with static data, then calibration time is reduced and processing efficiency is improved, but quantization error increases when actual input data differs from calibration data
Solution Approach 1:
The patent performs preliminary quantization calibration with static data to establish initial quantization parameters quickly, then supplements this with lightweight runtime adaptation mechanisms. The initial calibration provides a fast starting point, while subsequent automatic adjustments based on activation map monitoring ensure accuracy when input data features change, thus balancing calibration time investment with ongoing quantization accuracy without requiring extensive recalibration.
Data Source
AI summary
A quantization method of a deep learning neural network model is disclosed. An embodiment of the disclosure provides a quantization method comprising: detecting a feature change of input data caused by a change in an external environment, from input image data of a quantized deep learning neural network model based on a plurality of preset quantization parameters; performing quantization calibration for the deep learning neural network model to determine a new quantization parameter corresponding to the feature change of input data caused by the change in the external environment; and updating at least one of the plurality of preset quantization parameters based on the new quantization parameter.